Why is this work relevant for Media?

Data is being touted as new oil for a while now. It was a statement that we had been hearing but till it was executed, we have to admit that it was a vision that we hadn’t had before its implementation. Data theft and security were rising concerns, hence a solution to stop that was too. It is a relevant case for media because of it being a low involvement category whose awareness needed to have an exponential reach.

Background

To build awareness for the brand (data security & antivirus software) as well as build the category. With the growing population on digital and increased instances of data theft and security breaches, the brand wanted to expand its consumer base in India reaching out to the potential audience but through performance-driven media in a test, learn and scale-up mode. In a low involvement category of cyber security, building awareness about the product was important through mass media. The conduit of the campaign was to amplify a Digital-first campaign using TV as the performance media for incremental acquisition and optimizing business KPIs, CPVs (cost per visit) and CPOs (cost per orders).

Describe the creative idea / insights (30% of vote)

IGNORANCE CAN BE A BLISS, BUT NOT WHEN SECURITY IS AMISS When people buy PC/Laptops or mobile devices, they take the security apparatus of the system for granted. They are neither aware about what they have on their device, nor do they know whether it is pre-installed as per the recommendations / choice of the seller at retail outlet or because of other factors such as compatibility, efficiency. Traditionally, digital is the only medium that is being used as performance media in India however given the budget and the objective, we had to make use of traditional media as performance media to build the category as well as the brand. Additionally, TV has been used as a base media for building reach whereas for this campaign, we employed TV as a performance medium to drive ROI, over and above the baseline formed by digital initiatives.

Describe the strategy (20% of vote)

Response Attribution Modelling was used to build dynamic media planning by continuously implementing the insights/learnings coming through modelling which resulted in achieving efficient CPA across campaign and promo periods with continuous improved ROI. Calibrated the consumer journey for a TV Attribution model: from spot airing to visits to orders. We built the Attribution model using client’s real-time response data with spot-level TV spends data to use TV as a Digital+ performance media and bring down CPOs and CPVs. Used Brand’s response data of website visits and orders at an hourly level and mapped it back with spot list data from BARC for Tv and real-time data from Radio. The attribution model developed helped identify the genres, channels, time slots, programme, creative and edit size to help optimise the ROI in terms of driving quality leads.

Describe the execution (20% of vote)

Basis the learnings, we were able to create dynamic TV plans for each upcoming week based on the performance of genres, channels, and time bands, leading to continuously improved CPOs and CPVs across weeks. o Creative language and size of creative were tested based on the target audience. o Creative language testing was carried out in Regional vs English language basis the response it would generate. o Similarly, creative edit size was tested basis the response it generated. o Furthermore, we tested the creative fatigue for weeks. The campaign message was optimized for the target audience and implemented within the test market.

List the results (30% of vote)

• The overall CPO improved by approximately 10X while the CPV improved by 9X • The sales improved by more than 10X during promo period • Ad recall was highest for Norton during the campaign period and per third parted brand track study conducted by client • Norton topped the category for brand momentum • Norton was on top of the funnel metrics: Unaided Awareness and Familiarity orders. • Key shift in ROI metrics

Describe the use of data, or how the data enhanced the campaign output

•The traffic/orders that were considered for TV attribution were limited only to organic, direct, and paid search. We established a baseline from historic dates with response data from client. •Since the Response data is at hourly level, we removed the behavioural response at Day X Time band level by using baseline •Also, by dividing the spot level data into hourly slots, we secured several spots and investments for each channel across an hourly time band. •Calculated the weights for each channel at each spot basis the investments on a day X hourly level •Considering the total orders & visits, calculated revenue across hourly bands each day after removing the baseline. •Calculated the orders, visits, and revenue attribution for each of the channels at each slot, basis the weight for each channel. •Considered the cost & identified the CPO and CPV for each time band of every channel for a week